import sys
import time
import os
import json
import re
from datetime import datetime, timedelta
from loguru import logger

from src.utils.config_loader import load_config
from src.utils.logger import setup_logger
from src.data_collection.polymarket_api import PolymarketClient
from src.data_collection.weather_sources import WeatherDataCollector
from src.data_collection.onchain_tracker import OnchainTracker
from src.models.statistical_model import TemperaturePredictor
from src.analysis.volume_analyzer import VolumeAnalyzer
from src.analysis.orderbook_analyzer import OrderbookAnalyzer
from src.analysis.technical_indicators import TechnicalIndicators
from src.analysis.whale_tracker import WhaleTracker
from src.strategy.decision_engine import DecisionEngine
from src.strategy.risk_manager import RiskManager
from src.trading.paper_trader import PaperTrader
from src.utils.notifier import TelegramNotifier


def main():
    # 1. 初始化配置与日志
    config_data = load_config()
    setup_logger(config_data.get("app", {}).get("log_level", "INFO"))

    logger.info("🌟 PolyWeather 监控引擎启动中...")

    # 2. 初始化核心组件
    polymarket = PolymarketClient(config_data["polymarket"])
    weather = WeatherDataCollector(config_data["weather"])
    onchain = OnchainTracker(config_data["polymarket"], polymarket)
    notifier = TelegramNotifier(config_data["telegram"])

    # 3. 初始化分析与交易组件
    predictor = TemperaturePredictor()
    decision_engine = DecisionEngine(config_data.get("config", {}))
    whale_tracker = WhaleTracker(config_data.get("config", {}), onchain)
    paper_trader = PaperTrader()

    # 发送启动通知
    notifier._send_message(
        "🚀 <b>Polymarket 天气监控系统启动成功</b>\n正在扫描 12 个核心城市的最高温市场..."
    )

    # 信号记忆（持久化到文件）
    pushed_signals = {}
    SIGNALS_FILE = "data/pushed_signals.json"
    if os.path.exists(SIGNALS_FILE):
        try:
            with open(SIGNALS_FILE, "r", encoding="utf-8") as f:
                pushed_signals = json.load(f)
            logger.info(f"已加载历史推送记录，共 {len(pushed_signals)} 条")
        except:
            pushed_signals = {}

    # 确保data目录存在
    if not os.path.exists("data"):
        os.makedirs("data")

    location_cache = {}

    # 价格历史追踪（用于计算趋势）
    PRICE_HISTORY_FILE = "data/price_history.json"
    price_history = {}
    if os.path.exists(PRICE_HISTORY_FILE):
        try:
            with open(PRICE_HISTORY_FILE, "r", encoding="utf-8") as f:
                price_history = json.load(f)
        except:
            price_history = {}

    try:
        while True:
            logger.info("--- 开启新一轮全量动态监控 (自动搜寻所有天气市场) ---")
            cached_signals = {}
            all_markets_cache = {}

            # 1. 直接从 Polymarket 获取所有天气合约
            all_weather_markets = polymarket.get_weather_markets()

            # 1.5 尝试通过slug获取可能遗漏的市场（如部分结算的市场）
            special_slugs = []

            for slug in special_slugs:
                event = polymarket.get_event_by_slug(slug)
                if event:
                    title = event.get("title", "")
                    logger.info(f"通过slug找到特殊事件: {title}")

                    # 提取城市名
                    city = weather.extract_city_from_question(title)
                    if not city:
                        city = "Unknown"

                    # 将该事件的所有市场添加到列表
                    for m in event.get("markets", []):
                        # 检查是否已存在
                        c_id = m.get("conditionId")
                        if not any(
                            existing.get("condition_id") == c_id
                            for existing in all_weather_markets
                        ):
                            all_weather_markets.append(
                                {
                                    "condition_id": c_id,
                                    "question": m.get("groupItemTitle")
                                    or m.get("question"),
                                    "active_token_id": m.get("activeTokenId"),
                                    "tokens": m.get("clobTokenIds"),
                                    "prices": m.get("outcomePrices"),
                                    "event_title": title,
                                    "slug": slug,
                                    "city": city,  # 提前标记城市
                                }
                            )
                            logger.debug(f"添加特殊市场: {m.get('groupItemTitle')}")

            if not all_weather_markets:
                logger.warning("当前 Polymarket 似乎没有任何活跃的天气市场，等待中...")
                time.sleep(300)
                continue

            # 2. 批量同步盘口价格 (优化：为每个档位获取其对应的真实 Token 价格)
            token_price_map = {}
            price_requests = []
            for m in all_weather_markets:
                ts = m.get("tokens", [])
                if isinstance(ts, str):
                    try: ts = json.loads(ts)
                    except: ts = []
                
                active_tid = m.get("active_token_id")
                
                # 如果是多选一市场（比如 Dallas 76-77°F）
                if len(ts) > 2 and active_tid:
                    # 获取该档位的买入价 (Ask) 
                    price_requests.append({"token_id": active_tid, "side": "ask"})
                    # 获取该档位的买入“否”价所需的 Bid 价
                    price_requests.append({"token_id": active_tid, "side": "bid"})
                # 如果是传统的 Yes/No 二选一市场
                elif len(ts) == 2:
                    price_requests.append({"token_id": ts[0], "side": "ask"})  # Buy Yes
                    price_requests.append({"token_id": ts[1], "side": "ask"})  # Buy No

            if price_requests:
                logger.info(f"正在同步 {len(price_requests)} 个档位的真实盘口价格...")
                token_price_map = polymarket.get_multiple_prices(price_requests)
                logger.info(f"价格同步完成，成功获取 {len(token_price_map)} 个实时报价")

            # 3. 按城市分组（按condition_id去重）
            markets_by_city = {}
            seen_condition_ids = set()

            for i, m in enumerate(all_weather_markets):
                c_id = m.get("condition_id")
                if c_id in seen_condition_ids:
                    continue  # 跳过重复
                seen_condition_ids.add(c_id)

                # 注入实时批量价格
                ts = m.get("tokens", [])
                if isinstance(ts, str):
                    try: ts = json.loads(ts)
                    except: ts = []
                
                active_tid = m.get("active_token_id")
                
                # 多选一市场逻辑
                if len(ts) > 2 and active_tid:
                    m["buy_yes_live"] = token_price_map.get(f"{active_tid}:ask")
                    # 买入“否”的价格 = 1 - 该档位的 Bid
                    bid_val = token_price_map.get(f"{active_tid}:bid")
                    if bid_val:
                        m["buy_no_live"] = 1.0 - bid_val
                # 二选一市场逻辑
                elif len(ts) == 2:
                    m["buy_yes_live"] = token_price_map.get(f"{ts[0]}:ask")
                    m["buy_no_live"] = token_price_map.get(f"{ts[1]}:ask")

                # 优先使用发现阶段已经识别出的城市名
                city = m.get("city")

                # 如果发现阶段没识别出，再尝试从问题文本提取
                if not city or city == "Unknown":
                    full_context = f"{m.get('event_title', '')} {m.get('question', '')}"
                    city = weather.extract_city_from_question(full_context)

                if i < 5:
                    logger.debug(
                        f"分析合约 {i}: City='{city}' | Title='{m.get('event_title')}"
                    )

                if not city:
                    continue

                if city not in markets_by_city:
                    markets_by_city[city] = []
                markets_by_city[city].append(m)

            logger.info(
                f"动态发现 {len(markets_by_city)} 个受监控城市，共 {len(all_weather_markets)} 个合约"
            )

            # 3. 逐个城市分析
            for city, city_markets in markets_by_city.items():
                try:
                    # 获取/缓存坐标
                    if city not in location_cache:
                        coords = weather.get_coordinates(city)
                        if not coords:
                            continue
                        location_cache[city] = coords
                        logger.info(
                            f"📍 城市定位成功: {city} -> ({coords['lat']}, {coords['lon']})"
                        )

                    loc = location_cache[city]

                    # A. 获取实时天气共识
                    weather_data = weather.fetch_all_sources(
                        city, lat=loc["lat"], lon=loc["lon"]
                    )
                    consensus = weather.check_consensus(weather_data)

                    if not consensus.get("consensus"):
                        continue

                    logger.info(
                        f"☁️ {city} 当前气温: {consensus['average_temp']}°C | 监控合约: {len(city_markets)}"
                    )

                    # --- 本城市汇总预警缓存 ---
                    city_alerts = []
                    city_local_time = None

                    # B. 遍历该城市所有合约
                    for market in city_markets:
                        market_id = market.get("condition_id")
                        question = market.get("question", "未知市场")
                        event_title = market.get("event_title", "")

                        # 识别该合约的目标日期
                        target_date = weather.extract_date_from_title(
                            event_title
                        ) or weather.extract_date_from_title(question)
                        ref_temp = consensus["average_temp"]
                        if target_date:
                            daily_data = weather_data.get("open-meteo", {}).get(
                                "daily", {}
                            )
                            if daily_data:
                                dates = daily_data.get("time", [])
                                max_temps = daily_data.get("temperature_2m_max", [])
                                for idx, d_str in enumerate(dates):
                                    if target_date == d_str:
                                        ref_temp = max_temps[idx]
                                        break

                        # --- 价格获取逻辑 (增强版) ---
                        buy_yes_price = market.get("buy_yes_live")
                        buy_no_price = market.get("buy_no_live")
                        ts = market.get("tokens", [])
                        if isinstance(ts, str): ts = json.loads(ts)
                        active_tid = market.get("active_token_id")

                        # 特殊逻辑：针对多选一型市场（Dallas/Chicago 等温度段）
                        if len(ts) > 2 and active_tid:
                            # 1. 尝试从批量映射中重新获取该档位的精确买入价(Ask)
                            buy_yes_price = polymarket.get_price(active_tid, side="ask") if buy_yes_price is None else buy_yes_price
                            # 2. 计算“否”的价格（在多选一里是 1 - 最高买入意愿价Bid）
                            bid_price = polymarket.get_price(active_tid, side="bid")
                            if bid_price:
                                buy_no_price = 1.0 - bid_price
                            
                            if i < 3: # 仅对前几个档位打印调试
                                logger.debug(f"Categorical价格匹配 [{market.get('city')}-{market.get('question')}]: Yes={buy_yes_price}, No={buy_no_price} (from Bid={bid_price})")

                        # 通用回退逻辑 (二选一)
                        if buy_yes_price is None or buy_no_price is None:
                            if ts and len(ts) >= 2:
                                prices = polymarket.get_buy_prices(ts[0], ts[1])
                                if prices:
                                    buy_yes_price = prices.get("buy_yes")
                                    buy_no_price = prices.get("buy_no")
                        
                        # 最后的回退：使用原始 Gamma 概率 (利用 outcome_index 获取正确的那一个)
                        if buy_yes_price is None or buy_no_price is None:
                            gamma_prices = market.get("prices", [])
                            if isinstance(gamma_prices, str): gamma_prices = json.loads(gamma_prices)
                            
                            # 尝试获取该档位相对应的概率索引
                            idx = market.get("outcome_index", 0)
                            prob = float(gamma_prices[idx]) if (gamma_prices and idx < len(gamma_prices)) else 0.5
                            
                            if buy_yes_price is None: buy_yes_price = prob
                            if buy_no_price is None: buy_no_price = 1.0 - prob

                        # C. 准备缓存数据
                        temp_unit = weather_data.get("open-meteo", {}).get("unit", "celsius")
                        temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
                        city_local_time = weather_data.get("open-meteo", {}).get("current", {}).get("local_time")
                        
                        current_price = buy_yes_price if buy_yes_price else 0.5
                        
                        # 计算价格趋势
                        prev_data = price_history.get(market_id, {})
                        prev_price = prev_data.get("price", current_price)
                        price_change_pct = ((current_price - prev_price) / prev_price * 100) if prev_price > 0 else 0
                        
                        # 更新价格历史缓存
                        price_history[market_id] = {
                            "price": current_price,
                            "timestamp": datetime.now().isoformat()
                        }

                        cache_entry = {
                            "city": city,
                            "full_title": event_title,
                            "option": question,
                            "prediction": f"{ref_temp}{temp_symbol}",
                            "price": int(current_price * 100),
                            "buy_yes": int(buy_yes_price * 100) if buy_yes_price else 0,
                            "buy_no": int(buy_no_price * 100) if buy_no_price else 0,
                            "url": f"https://polymarket.com/event/{market.get('slug')}",
                            "local_time": city_local_time,
                            "target_date": target_date,
                            "score": 0,
                            "rationale": "ACTIVE",
                            "trend": round(price_change_pct, 1),
                        }

                        # --- 最终过滤器 (拦截垃圾信号) ---
                        
                        # 1. 过滤已锁定价格 (>= 98.5c)
                        if (buy_yes_price and buy_yes_price >= 0.985) or (buy_no_price and buy_no_price >= 0.985):
                            cache_entry["rationale"] = "ENDED"
                            all_markets_cache[market_id] = cache_entry
                            continue

                        # 2. 过滤已过期日期 (对比当前日期: 2026-02-06)
                        current_today = "2026-02-06"
                        if target_date and target_date < current_today:
                            cache_entry["rationale"] = "EXPIRED"
                            all_markets_cache[market_id] = cache_entry
                            continue

                        # 3. 评分计算
                        try:
                            signal = decision_engine.calculate_signal(
                                model_prediction=predictor.predict_ensemble([ref_temp]),
                                market_data={
                                    "orderbook": {},
                                    "price_history": [current_price],
                                    "transactions": [],
                                },
                                weather_consensus={"average_temp": ref_temp},
                                whale_activity=None,
                            )
                            cache_entry["score"] = signal.get("final_score", 0)
                            cache_entry["rationale"] = signal.get("recommendation", "ACTIVE")
                        except Exception as e:
                            logger.error(f"计算信号失败 [{market_id}]: {e}")
                            cache_entry["score"] = 0
                            cache_entry["rationale"] = "ERROR"

                        all_markets_cache[market_id] = cache_entry

                        # --- 预警收集 (自动推送逻辑) ---
                        if (buy_yes_price and 0.85 <= buy_yes_price <= 0.95) or (buy_no_price and 0.85 <= buy_no_price <= 0.95):
                            alert_key = f"alert_{market_id}_range_85_95"
                            if alert_key not in pushed_signals:
                                trigger_side = (
                                    "Buy Yes" if buy_yes_price >= 0.85 else "Buy No"
                                )
                                trigger_price = (
                                    int(buy_yes_price * 100)
                                    if trigger_side == "Buy Yes"
                                    else int(buy_no_price * 100)
                                )

                                # --- 智能动态仓位计算 ---
                                # 1. 获取 Open-Meteo 对目标日期的最高温预测
                                predicted_high = None
                                weather_supports = False
                                daily_data = weather_data.get("open-meteo", {}).get("daily", {})
                                if daily_data and target_date:
                                    dates = daily_data.get("time", [])
                                    max_temps = daily_data.get("temperature_2m_max", [])
                                    for idx, d_str in enumerate(dates):
                                        if target_date == d_str and idx < len(max_temps):
                                            predicted_high = max_temps[idx]
                                            break
                                
                                # 2. 判断天气预测是否支持当前方向
                                if predicted_high is not None:
                                    # 解析选项的温度范围 (例如 "40-41°F" 或 "32°F or below")
                                    temp_match = re.search(r'(\d+)(?:-(\d+))?°[FC]', question)
                                    if temp_match:
                                        low_bound = int(temp_match.group(1))
                                        high_bound = int(temp_match.group(2)) if temp_match.group(2) else low_bound
                                        
                                        # 如果买 NO，天气预测应该在这个区间之外
                                        if trigger_side == "Buy No":
                                            weather_supports = (predicted_high < low_bound - 2) or (predicted_high > high_bound + 2)
                                        else:  # 买 YES
                                            weather_supports = (low_bound - 2 <= predicted_high <= high_bound + 2)
                                
                                # 3. 获取成交量信息
                                market_volume = market.get("volume", 0)
                                if isinstance(market_volume, str):
                                    try:
                                        market_volume = float(market_volume.replace("$", "").replace(",", ""))
                                    except:
                                        market_volume = 0
                                high_volume = market_volume >= 5000  # $5000+ 算高成交量
                                
                                # 4. 动态仓位决策
                                # 条件: 价格锁定程度 + 天气支持 + 成交量
                                if trigger_price >= 90 and weather_supports and high_volume:
                                    # 三重确认：重注
                                    amount_usd = 10.0
                                    confidence_tag = "🔥高置信"
                                elif trigger_price >= 90 and weather_supports:
                                    # 双重确认：中等仓位
                                    amount_usd = 7.0
                                    confidence_tag = "⭐中置信"
                                elif trigger_price >= 92:
                                    # 价格接近锁定，即使其他条件不满足也小额参与
                                    amount_usd = 5.0
                                    confidence_tag = "📌价格锁定"
                                else:
                                    # 普通信号：最小仓位
                                    amount_usd = 3.0
                                    confidence_tag = "💡试探"
                                
                                logger.info(
                                    f"【仓位决策】{city} {question} | "
                                    f"价格:{trigger_price}¢ | 预测:{predicted_high} | 天气支持:{weather_supports} | "
                                    f"高量:{high_volume} | 仓位:${amount_usd} ({confidence_tag})"
                                )

                                # --- 模拟交易触发逻辑 ---
                                side = "YES" if trigger_side == "Buy Yes" else "NO"
                                success = paper_trader.open_position(
                                    market_id=market_id,
                                    city=city,
                                    option=question,
                                    price=trigger_price,
                                    side=side,
                                    amount_usd=amount_usd,
                                    target_date=target_date,
                                    predicted_temp=predicted_high,
                                )

                                # 构建预测温度显示文本
                                temp_unit = weather_data.get("open-meteo", {}).get("unit", "celsius")
                                temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
                                forecast_text = f"预测:{predicted_high}{temp_symbol}" if predicted_high else "预测:N/A"

                                city_alerts.append(
                                    {
                                        "type": "price",
                                        "market": f"{question} ({target_date or '今日'})",
                                        "msg": f"{trigger_side} {trigger_price}¢ | {forecast_text}",
                                        "bought": success,
                                        "amount": amount_usd,
                                        "confidence": confidence_tag,
                                    }
                                )
                                pushed_signals[alert_key] = time.time()

                        # 3. 信号暂存
                        cached_signals[market_id] = cache_entry

                    # --- 循环结束后统一推送本城市汇总 ---
                    notifier.send_combined_alert(
                        city, city_alerts, local_time=city_local_time
                    )

                except Exception as e:
                    logger.error(f"分析城市 {city} 时出错: {e}")
                    continue

                # --- 每处理完一个城市，立即更新 JSON 文件 ---
                try:
                    # --- 周期性结算：保存高价值信号 ---
                    active_signals = []
                    for mid, entry in all_markets_cache.items():
                        # 核心过滤：只有 ACTIVE 且 价格未锁定、日期未过期的才进入 signals 列表
                        if entry.get("rationale") not in ["ENDED", "EXPIRED", "ERROR"]:
                            # 再次双重检查日期 (硬核拦截 2026-02-06)
                            target_dt = entry.get("target_date")
                            if target_dt and target_dt < "2026-02-06":
                                continue
                            active_signals.append(entry)
                    
                    # 按分数排序
                    active_signals.sort(key=lambda x: x.get("score", 0), reverse=True)
                    
                    with open("data/active_signals.json", "w", encoding="utf-8") as f:
                        json.dump(active_signals, f, ensure_ascii=False, indent=4)
                    
                    logger.info(f"已更新活跃信号库，包含 {len(active_signals)} 个有效信号。")
                    
                    # 2. 更新全量市场缓存
                    try:
                        with open("data/all_markets.json", "r", encoding="utf-8") as f:
                            existing_markets = json.load(f)
                    except:
                        existing_markets = {}

                    existing_markets.update(all_markets_cache)

                    # 清理过期日期
                    today_str = datetime.now().strftime("%Y-%m-%d")
                    cleaned_markets = {}
                    for k, v in existing_markets.items():
                        t_date = v.get("target_date")
                        if not t_date or t_date >= today_str:
                            cleaned_markets[k] = v

                    with open("data/all_markets.json", "w", encoding="utf-8") as f:
                        json.dump(cleaned_markets, f, ensure_ascii=False, indent=2)

                    # 3. 保存推送记录
                    with open("data/pushed_signals.json", "w", encoding="utf-8") as f:
                        json.dump(pushed_signals, f, ensure_ascii=False)

                    # 3.5 保存价格历史（用于趋势计算）
                    with open(PRICE_HISTORY_FILE, "w", encoding="utf-8") as f:
                        json.dump(price_history, f, ensure_ascii=False)

                    # --- 4. 更新模拟仓位盈亏 ---
                    price_snapshot = {}
                    for mid, entry in all_markets_cache.items():
                        price_snapshot[mid] = {"price": entry["price"]}
                    paper_trader.update_pnl(price_snapshot)

                    # --- 5. 每日收益总结推送 (北京时间 23:55 - 00:05 之间发送) ---
                    now_bj = datetime.utcnow() + timedelta(hours=8)
                    if now_bj.hour == 23 and now_bj.minute >= 50:
                        summary_key = f"daily_pnl_{now_bj.strftime('%Y%m%d')}"
                        if summary_key not in pushed_signals:
                            # 构造总结消息
                            total_cost = 0
                            total_pnl = 0
                            data = paper_trader._load_data()
                            pos_list = data.get("positions", {})

                            if pos_list:
                                report = [
                                    f"📊 <b>每日模拟仓结算总结 ({now_bj.strftime('%Y-%m-%d')})</b>\n"
                                    + "═" * 15
                                ]
                                for p in pos_list.values():
                                    if p["status"] == "OPEN":
                                        total_cost += p["cost_usd"]
                                        total_pnl += p.get("pnl_usd", 0)

                                report.append(
                                    f"💳 可用余额: <b>${data.get('balance', 0):.2f}</b>"
                                )
                                report.append(
                                    f"💰 今日累计投入: <b>${total_cost:.2f}</b>"
                                )
                                report.append(
                                    f"📈 累计浮动盈亏: <b>{total_pnl:+.2f}$</b>"
                                )
                                notifier._send_message("\n".join(report))
                                pushed_signals[summary_key] = time.time()

                except Exception as e:
                    logger.error(f"即时保存数据失败: {e}")

            logger.info("本轮扫描结束。等待 5 分钟...")
            time.sleep(300)

    except KeyboardInterrupt:
        logger.info("收到关机指令，正在退出...")
    except Exception as e:
        logger.exception(f"系统运行出错: {e}")


if __name__ == "__main__":
    main()